timmy / 使用 Streamlit 建立 YAML 員工資料管理系統

employees: - Age: 34 City: New York Name: Alice Occupation: Engineer Salary: 70000 - Age: 27 City: Los Angeles Name: Bob Occupation: Doctor Salary: 120000 - Age: 22 City: Chicago Name: Charlie Occupation: Artist Salary: 50000

import streamlit as st import pandas as pd import yaml from yaml.loader import SafeLoader # 讀取 YAML 檔案 def read_yaml(file_path): with open(file_path, 'r') as file: data = yaml.load(file, Loader=SafeLoader) return data # 寫入 YAML 檔案 def write_yaml(file_path, data): with open(file_path, 'w') as file: yaml.dump(data, file) # 讀取資料 yaml_file = 'data.yaml' data = read_yaml(yaml_file) # 轉換資料為 DataFrame df = pd.DataFrame(data['employees']) # 顯示 DataFrame st.write('## 員工資料表') st.write(df) # 允許用戶編輯資料，並隱藏 City 欄位 st.write('## 編輯員工資料') edited_df = st.data_editor(df.drop(columns=['City'])) # 當用戶提交修改後，將資料寫回 YAML 檔案，但要保留 City 欄位 if st.button('儲存修改'): edited_df['City'] = df['City'] # 將原始資料中的 City 欄位加回來 data['employees'] = edited_df.to_dict(orient='records') write_yaml(yaml_file, data) st.success('修改成功儲存！') # 隱藏某些欄位 columns_to_hide = ['Salary'] df_visible = df.drop(columns=columns_to_hide) # 顯示隱藏某些欄位後的 DataFrame st.write('## 員工資料表 (過濾後)') st.write(df_visible) # 互動元素：滑桿 age_filter = st.slider('選擇年齡範圍', 20, 30, (20, 30)) # 篩選 DataFrame filtered_df = df_visible[(df_visible['Age'] >= age_filter[0]) & (df_visible['Age'] <= age_filter[1])] # 顯示篩選後的 DataFrame st.write('## 過濾後的員工資料表') st.write(filtered_df)